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        "%matplotlib inline"
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        "\n# Plot the decision boundaries of a VotingClassifier\n\n\n.. currentmodule:: sklearn\n\nPlot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two\nfeatures of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset predicted by\nthree different classifiers and averaged by the\n:class:`~ensemble.VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized\n(:class:`~tree.DecisionTreeClassifier`,\n:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to\ninitialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,\n1, 2]`, which means that the predicted probabilities of the\n:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times\nas much as the weights of the :class:`~neighbors.KNeighborsClassifier`\nclassifier when the averaged probability is calculated.\n\n\n"
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      "source": [
        "print(__doc__)\n\nfrom itertools import product\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import VotingClassifier\n\n# Loading some example data\niris = datasets.load_iris()\nX = iris.data[:, [0, 2]]\ny = iris.target\n\n# Training classifiers\nclf1 = DecisionTreeClassifier(max_depth=4)\nclf2 = KNeighborsClassifier(n_neighbors=7)\nclf3 = SVC(gamma=.1, kernel='rbf', probability=True)\neclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2),\n                                    ('svc', clf3)],\n                        voting='soft', weights=[2, 1, 2])\n\nclf1.fit(X, y)\nclf2.fit(X, y)\nclf3.fit(X, y)\neclf.fit(X, y)\n\n# Plotting decision regions\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\nxx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n                     np.arange(y_min, y_max, 0.1))\n\nf, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n\nfor idx, clf, tt in zip(product([0, 1], [0, 1]),\n                        [clf1, clf2, clf3, eclf],\n                        ['Decision Tree (depth=4)', 'KNN (k=7)',\n                         'Kernel SVM', 'Soft Voting']):\n\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n    Z = Z.reshape(xx.shape)\n\n    axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)\n    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y,\n                                  s=20, edgecolor='k')\n    axarr[idx[0], idx[1]].set_title(tt)\n\nplt.show()"
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